Questiond about the descrepancy between edgeR and limma-voom
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Yunshun Chen ▴ 840
@yunshun-chen-5451
Last seen 8 days ago
Australia
Hi Ming, The edgeR likelihood ratio test (glmLRT) is slightly liberal in general, hence gives much more DEGs. You can try the quasi-likelihood F-test in edgeR instead, and then compare the results with limma-voom. Also, the genewise dispersions can be estimated in one step using estimateDisp(). See the followings: > y <- estimateDisp(y, design) > qlf <- glmQLFTest(y, design, con.matrix) > show(summary(de <- decideTestsDGE(qlf))) Regards, Yunshun ---------------------------------------------------------------------- Message: 24. Questiond about the descrepancy between edgeR and limma- voom (Ming Yi) Hi, Dear all: I am looking for advice for what I observed in my analysis results using the same data but two very close related methods: limma-voom and edgeR. for purpose of consolidation and comparison, since I try to get as accurate DEGs lists for my next step downstream analysis, for the same dataset, I used both edgeR and limma-voom originally try consolidate each other. Surprisingly, as shown below the numbers of DEGs for 4 constrasts I am interested, there are consistency between the results of normal contrasts: e.g., for contrasts RasOnly.Normal_RasPNot.Normal and RasP.Normal_RasPNot.Normal (both involved comparison of normal samples of different types) in terms of numbers of DEGs (in very close ranges), however, there are large discrepancy between the results of tumor contrasts: e.g., RasP.Tumor_RasPNot.Tumor and RasOnly.Tumor_RasPNot.Tumor. The edgeR result has about 1k(416+420) DEGs for RasP.Tumor_RasPNot.Tumor and 890+552=1.3k DEGs for RasOnly.Tumor_RasPNot.Tumor at FDR 5%, whereas limma-voom method only picked up a bit more than 100 or less DEGs for the same contrasts (almost no DEGs at the default cutoff FDR or adjusted p-value level) shown below. Of course, maybe cutoffs (e.g., FDR vs adjusted.p-value) or model may have different impacts in the DEGs sets,however, the odd thing is: for the same cutoffs, we did see quite consistency in normal contrasts, but large dsicrepancy in the tumor contrasts. From almost no DEG (or a bit more than 100) to more than 1k DEGs seem a big difference to me. Since that would impact the downstream analysis such as pathway analysis etc. The other main difference is for the same data, which are claimed raw counts of RSEM for RNAseq data, were rounded to integers for edgeR and also we used the original RSEM raw count values for limma-voom. Both methods used the identical makeContrasts commands shown below. Any suggestions and advice would be highly appreciated. Thanks a lot in advance! Ming ATRF NCI-Frederick Maryland, USA EdgR result: currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); show(summary(de <- decideTestsDGE(currLrt))); [1] "RasP.Tumor_RasPNot.Tumor" -1 416 0 17414 1 420 [1] "RasOnly.Tumor_RasPNot.Tumor" [,1] -1 890 0 16808 1 552 [1] "RasOnly.Normal_RasPNot.Normal" [,1] -1 4 0 18229 1 17 [1] "RasP.Normal_RasPNot.Normal" [,1] -1 481 0 16936 1 833 limma-voom result: > show(summary(de <- decideTests(fit))); RasP.Tumor_RasPNot.Tumor RasOnly.Tumor_RasPNot.Tumor -1 29 28 0 18202 18125 1 19 97 RasOnly.Normal_RasPNot.Normal RasP.Normal_RasPNot.Normal -1 0 682 0 18250 17184 1 0 384 critcal commands: EdgR: > y <- estimateGLMCommonDisp(y, design, verbose=TRUE); Disp = 0.42594 , BCV = 0.6526 > y <- estimateGLMTrendedDisp(y, design); Loading required package: splines > y <- estimateGLMTagwiseDisp(y, design) > fit <- glmFit(y, design); > con.matrix<-makeContrasts( + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, + levels=design) currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); show(summary(de <- decideTestsDGE(currLrt))); limma-voom: >design <- model.matrix(~0+RasTum); > con.matrix<-makeContrasts( + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, + levels=design) > fit<- lmFit(v,design) > fit<-contrasts.fit(fit, con.matrix) > fit <- eBayes(fit) > show(summary(de <- decideTests(fit))); [[alternative HTML version deleted]] ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
RNASeq edgeR RNASeq edgeR • 2.1k views
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Ming ▴ 380
@ming-yi-6363
Last seen 19 months ago
United States
Hi,Yunshun: Thx so much for your advice, appreciated very much! I will give a shot. Best, Ming > From: yuchen@wehi.EDU.AU > To: yi02@hotmail.com > CC: bioconductor@r-project.org > Subject: RE: [BioC] Questiond about the descrepancy between edgeR and limma-voom > Date: Mon, 3 Mar 2014 11:17:43 +1100 > > Hi Ming, > > The edgeR likelihood ratio test (glmLRT) is slightly liberal in general, > hence gives much more DEGs. > You can try the quasi-likelihood F-test in edgeR instead, and then compare > the results with limma-voom. > > Also, the genewise dispersions can be estimated in one step using > estimateDisp(). > See the followings: > > > y <- estimateDisp(y, design) > > qlf <- glmQLFTest(y, design, con.matrix) > > show(summary(de <- decideTestsDGE(qlf))) > > Regards, > Yunshun > > > ---------------------------------------------------------------------- > > Message: 24. Questiond about the descrepancy between edgeR and limma-voom > (Ming Yi) > > Hi, Dear all: > > I am looking for advice for what I observed in my analysis results using > the same data but two very close related methods: limma-voom and edgeR. > for purpose of consolidation and comparison, since I try to get as accurate > DEGs lists for my next step downstream analysis, for the same dataset, I > used both edgeR and limma-voom originally try consolidate each other. > Surprisingly, as shown below the numbers of DEGs for 4 constrasts I am > interested, there are consistency between the results of normal contrasts: > e.g., for contrasts RasOnly.Normal_RasPNot.Normal and > RasP.Normal_RasPNot.Normal (both involved comparison of normal samples of > different types) in terms of numbers of DEGs (in very close ranges), > however, there are large discrepancy between the results of tumor contrasts: > e.g., RasP.Tumor_RasPNot.Tumor and RasOnly.Tumor_RasPNot.Tumor. The edgeR > result has about 1k(416+420) DEGs for RasP.Tumor_RasPNot.Tumor and > 890+552=1.3k DEGs for RasOnly.Tumor_RasPNot.Tumor at FDR 5%, whereas > limma-voom method only picked up a bit more than 100 or less DEGs for the > same contrasts (almost no DEGs at the default cutoff FDR or adjusted p-value > level) shown below. Of course, maybe cutoffs (e.g., FDR vs adjusted.p-value) > or model may have different impacts in the DEGs sets,however, the odd thing > is: for the same cutoffs, we did see quite consistency in normal contrasts, > but large dsicrepancy in the tumor contrasts. From almost no DEG (or a bit > more than 100) to more than 1k DEGs seem a big difference to me. Since that > would impact the downstream analysis such as pathway analysis etc. The other > main difference is for the same data, which are claimed raw counts of RSEM > for RNAseq data, were rounded to integers for edgeR and also we used the > original RSEM raw count values for limma-voom. Both methods used the > identical makeContrasts commands shown below. Any suggestions and advice > would be highly appreciated. Thanks a lot in advance! > > Ming > ATRF > NCI-Frederick > Maryland, USA > > EdgR result: > currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); > show(summary(de <- decideTestsDGE(currLrt))); > [1] "RasP.Tumor_RasPNot.Tumor" > -1 416 > 0 17414 > 1 420 > > [1] "RasOnly.Tumor_RasPNot.Tumor" > [,1] > -1 890 > 0 16808 > 1 552 > > [1] "RasOnly.Normal_RasPNot.Normal" > [,1] > -1 4 > 0 18229 > 1 17 > > [1] "RasP.Normal_RasPNot.Normal" > [,1] > -1 481 > 0 16936 > 1 833 > > limma-voom result: > > show(summary(de <- decideTests(fit))); > RasP.Tumor_RasPNot.Tumor RasOnly.Tumor_RasPNot.Tumor > -1 29 28 > 0 18202 18125 > 1 19 97 > RasOnly.Normal_RasPNot.Normal RasP.Normal_RasPNot.Normal > -1 0 682 > 0 18250 17184 > 1 0 384 > > critcal commands: > EdgR: > > y <- estimateGLMCommonDisp(y, design, verbose=TRUE); > Disp = 0.42594 , BCV = 0.6526 > > y <- estimateGLMTrendedDisp(y, design); > Loading required package: splines > > y <- estimateGLMTagwiseDisp(y, design) > > fit <- glmFit(y, design); > > con.matrix<-makeContrasts( > + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, > + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, > + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, > + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, > + levels=design) > currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); > show(summary(de <- decideTestsDGE(currLrt))); > > limma-voom: > >design <- model.matrix(~0+RasTum); > > con.matrix<-makeContrasts( > + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, > + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, > + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, > + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, > + levels=design) > > fit<- lmFit(v,design) > > fit<-contrasts.fit(fit, con.matrix) > > fit <- eBayes(fit) > > show(summary(de <- decideTests(fit))); > > > [[alternative HTML version deleted]] > > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:9}}
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Ming ▴ 380
@ming-yi-6363
Last seen 19 months ago
United States
Hi, YunShun: I did try the quasi test using your code and did change some of my original codes a bit (use the code you suggested and also I took out the three estimate commands for GLM): here is what I did >y <- estimateDisp(y, design) > qlf <- glmQLFTest(y, design, con.matrix) Error in glmFit.default(y = y$counts, design = design, dispersion = dispersion, : Length of dispersion vector incompatible with count matrix. Dispersion argument must be either of length 1 (i.e. common dispersion) or length equal to the number of rows of y (i.e. individual dispersion value for each tag/gene). I checked the manual of glmQLFTest and so I used the qlf <- glmQLFTest(y, design,contrast= con.matrix), which seem working. Then I tried the command you suggested: show(summary(de <- decideTestsDGE(qlf))) and I got error message again, the qlf object seems not work with the command show(summary(de <- decideTestsDGE(qlf))): > show(summary(de <- decideTestsDGE(qlf))) Error in show(summary(de <- decideTestsDGE(qlf))) : error in evaluating the argument 'object' in selecting a method for function 'show': Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL' I also tried what I usually do in limma-voom and also got error: > show(summary(de <- decideTests(qlf))) Error in show(summary(de <- decideTests(qlf))) : error in evaluating the argument 'object' in selecting a method for function 'show': Error in decideTests(qlf) : Need MArrayLM object Any idea? Thx and best Ming > From: yuchen@wehi.EDU.AU > To: yi02@hotmail.com > CC: bioconductor@r-project.org > Subject: RE: [BioC] Questiond about the descrepancy between edgeR and limma-voom > Date: Mon, 3 Mar 2014 11:17:43 +1100 > > Hi Ming, > > The edgeR likelihood ratio test (glmLRT) is slightly liberal in general, > hence gives much more DEGs. > You can try the quasi-likelihood F-test in edgeR instead, and then compare > the results with limma-voom. > > Also, the genewise dispersions can be estimated in one step using > estimateDisp(). > See the followings: > > > y <- estimateDisp(y, design) > > qlf <- glmQLFTest(y, design, con.matrix) > > show(summary(de <- decideTestsDGE(qlf))) > > Regards, > Yunshun > > > ---------------------------------------------------------------------- > > Message: 24. Questiond about the descrepancy between edgeR and limma-voom > (Ming Yi) > > Hi, Dear all: > > I am looking for advice for what I observed in my analysis results using > the same data but two very close related methods: limma-voom and edgeR. > for purpose of consolidation and comparison, since I try to get as accurate > DEGs lists for my next step downstream analysis, for the same dataset, I > used both edgeR and limma-voom originally try consolidate each other. > Surprisingly, as shown below the numbers of DEGs for 4 constrasts I am > interested, there are consistency between the results of normal contrasts: > e.g., for contrasts RasOnly.Normal_RasPNot.Normal and > RasP.Normal_RasPNot.Normal (both involved comparison of normal samples of > different types) in terms of numbers of DEGs (in very close ranges), > however, there are large discrepancy between the results of tumor contrasts: > e.g., RasP.Tumor_RasPNot.Tumor and RasOnly.Tumor_RasPNot.Tumor. The edgeR > result has about 1k(416+420) DEGs for RasP.Tumor_RasPNot.Tumor and > 890+552=1.3k DEGs for RasOnly.Tumor_RasPNot.Tumor at FDR 5%, whereas > limma-voom method only picked up a bit more than 100 or less DEGs for the > same contrasts (almost no DEGs at the default cutoff FDR or adjusted p-value > level) shown below. Of course, maybe cutoffs (e.g., FDR vs adjusted.p-value) > or model may have different impacts in the DEGs sets,however, the odd thing > is: for the same cutoffs, we did see quite consistency in normal contrasts, > but large dsicrepancy in the tumor contrasts. From almost no DEG (or a bit > more than 100) to more than 1k DEGs seem a big difference to me. Since that > would impact the downstream analysis such as pathway analysis etc. The other > main difference is for the same data, which are claimed raw counts of RSEM > for RNAseq data, were rounded to integers for edgeR and also we used the > original RSEM raw count values for limma-voom. Both methods used the > identical makeContrasts commands shown below. Any suggestions and advice > would be highly appreciated. Thanks a lot in advance! > > Ming > ATRF > NCI-Frederick > Maryland, USA > > EdgR result: > currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); > show(summary(de <- decideTestsDGE(currLrt))); > [1] "RasP.Tumor_RasPNot.Tumor" > -1 416 > 0 17414 > 1 420 > > [1] "RasOnly.Tumor_RasPNot.Tumor" > [,1] > -1 890 > 0 16808 > 1 552 > > [1] "RasOnly.Normal_RasPNot.Normal" > [,1] > -1 4 > 0 18229 > 1 17 > > [1] "RasP.Normal_RasPNot.Normal" > [,1] > -1 481 > 0 16936 > 1 833 > > limma-voom result: > > show(summary(de <- decideTests(fit))); > RasP.Tumor_RasPNot.Tumor RasOnly.Tumor_RasPNot.Tumor > -1 29 28 > 0 18202 18125 > 1 19 97 > RasOnly.Normal_RasPNot.Normal RasP.Normal_RasPNot.Normal > -1 0 682 > 0 18250 17184 > 1 0 384 > > critcal commands: > EdgR: > > y <- estimateGLMCommonDisp(y, design, verbose=TRUE); > Disp = 0.42594 , BCV = 0.6526 > > y <- estimateGLMTrendedDisp(y, design); > Loading required package: splines > > y <- estimateGLMTagwiseDisp(y, design) > > fit <- glmFit(y, design); > > con.matrix<-makeContrasts( > + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, > + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, > + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, > + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, > + levels=design) > currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); > show(summary(de <- decideTestsDGE(currLrt))); > > limma-voom: > >design <- model.matrix(~0+RasTum); > > con.matrix<-makeContrasts( > + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, > + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, > + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, > + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, > + levels=design) > > fit<- lmFit(v,design) > > fit<-contrasts.fit(fit, con.matrix) > > fit <- eBayes(fit) > > show(summary(de <- decideTests(fit))); > > > [[alternative HTML version deleted]] > > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:9}}
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Hi Ming, Sorry, the decideTestsDGE() in edgeR won't give you the same output format (one column for each contrast) as the decideTests() in limma-voom. I think you have to take each column of your contrast matrix and do the quasi-likelihood F-test separately, and then combine the results from each test (like what you did with glmLRT). Regards, Yunshun From: Ming Yi [mailto:yi02@hotmail.com] Sent: Wednesday, 5 March 2014 4:18 AM To: yuchen@wehi.EDU.AU Cc: Bioconductor mailing list Subject: RE: [BioC] Questiond about the descrepancy between edgeR and limma-voom Hi, YunShun: I did try the quasi test using your code and did change some of my original codes a bit (use the code you suggested and also I took out the three estimate commands for GLM): here is what I did >y <- estimateDisp(y, design) > qlf <- glmQLFTest(y, design, con.matrix) Error in glmFit.default(y = y$counts, design = design, dispersion = dispersion, : Length of dispersion vector incompatible with count matrix. Dispersion argument must be either of length 1 (i.e. common dispersion) or length equal to the number of rows of y (i.e. individual dispersion value for each tag/gene). I checked the manual of glmQLFTest and so I used the qlf <- glmQLFTest(y, design,contrast= con.matrix), which seem working. Then I tried the command you suggested: show(summary(de <- decideTestsDGE(qlf))) and I got error message again, the qlf object seems not work with the command show(summary(de <- decideTestsDGE(qlf))): > show(summary(de <- decideTestsDGE(qlf))) Error in show(summary(de <- decideTestsDGE(qlf))) : error in evaluating the argument 'object' in selecting a method for function 'show': Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL' I also tried what I usually do in limma-voom and also got error: > show(summary(de <- decideTests(qlf))) Error in show(summary(de <- decideTests(qlf))) : error in evaluating the argument 'object' in selecting a method for function 'show': Error in decideTests(qlf) : Need MArrayLM object Any idea? Thx and best Ming > From: yuchen@wehi.EDU.AU > To: yi02@hotmail.com > CC: bioconductor@r-project.org > Subject: RE: [BioC] Questiond about the descrepancy between edgeR and limma-voom > Date: Mon, 3 Mar 2014 11:17:43 +1100 > > Hi Ming, > > The edgeR likelihood ratio test (glmLRT) is slightly liberal in general, > hence gives much more DEGs. > You can try the quasi-likelihood F-test in edgeR instead, and then compare > the results with limma-voom. > > Also, the genewise dispersions can be estimated in one step using > estimateDisp(). > See the followings: > > > y <- estimateDisp(y, design) > > qlf <- glmQLFTest(y, design, con.matrix) > > show(summary(de <- decideTestsDGE(qlf))) > > Regards, > Yunshun > > > ---------------------------------------------------------------------- > > Message: 24. Questiond about the descrepancy between edgeR and limma-voom > (Ming Yi) > > Hi, Dear all: > > I am looking for advice for what I observed in my analysis results using > the same data but two very close related methods: limma-voom and edgeR. > for purpose of consolidation and comparison, since I try to get as accurate > DEGs lists for my next step downstream analysis, for the same dataset, I > used both edgeR and limma-voom originally try consolidate each other. > Surprisingly, as shown below the numbers of DEGs for 4 constrasts I am > interested, there are consistency between the results of normal contrasts: > e.g., for contrasts RasOnly.Normal_RasPNot.Normal and > RasP.Normal_RasPNot.Normal (both involved comparison of normal samples of > different types) in terms of numbers of DEGs (in very close ranges), > however, there are large discrepancy between the results of tumor contrasts: > e.g., RasP.Tumor_RasPNot.Tumor and RasOnly.Tumor_RasPNot.Tumor. The edgeR > result has about 1k(416+420) DEGs for RasP.Tumor_RasPNot.Tumor and > 890+552=1.3k DEGs for RasOnly.Tumor_RasPNot.Tumor at FDR 5%, whereas > limma-voom method only picked up a bit more than 100 or less DEGs for the > same contrasts (almost no DEGs at the default cutoff FDR or adjusted p-value > level) shown below. Of course, maybe cutoffs (e.g., FDR vs adjusted.p-value) > or model may have different impacts in the DEGs sets,however, the odd thing > is: for the same cutoffs, we did see quite consistency in normal contrasts, > but large dsicrepancy in the tumor contrasts. From almost no DEG (or a bit > more than 100) to more than 1k DEGs seem a big difference to me. Since that > would impact the downstream analysis such as pathway analysis etc. The other > main difference is for the same data, which are claimed raw counts of RSEM > for RNAseq data, were rounded to integers for edgeR and also we used the > original RSEM raw count values for limma-voom. Both methods used the > identical makeContrasts commands shown below. Any suggestions and advice > would be highly appreciated. Thanks a lot in advance! > > Ming > ATRF > NCI-Frederick > Maryland, USA > > EdgR result: > currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); > show(summary(de <- decideTestsDGE(currLrt))); > [1] "RasP.Tumor_RasPNot.Tumor" > -1 416 > 0 17414 > 1 420 > > [1] "RasOnly.Tumor_RasPNot.Tumor" > [,1] > -1 890 > 0 16808 > 1 552 > > [1] "RasOnly.Normal_RasPNot.Normal" > [,1] > -1 4 > 0 18229 > 1 17 > > [1] "RasP.Normal_RasPNot.Normal" > [,1] > -1 481 > 0 16936 > 1 833 > > limma-voom result: > > show(summary(de <- decideTests(fit))); > RasP.Tumor_RasPNot.Tumor RasOnly.Tumor_RasPNot.Tumor > -1 29 28 > 0 18202 18125 > 1 19 97 > RasOnly.Normal_RasPNot.Normal RasP.Normal_RasPNot.Normal > -1 0 682 > 0 18250 17184 > 1 0 384 > > critcal commands: > EdgR: > > y <- estimateGLMCommonDisp(y, design, verbose=TRUE); > Disp = 0.42594 , BCV = 0.6526 > > y <- estimateGLMTrendedDisp(y, design); > Loading required package: splines > > y <- estimateGLMTagwiseDisp(y, design) > > fit <- glmFit(y, design); > > con.matrix<-makeContrasts( > + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, > + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, > + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, > + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, > + levels=design) > currLrt<-glmLRT(fit, contrast=con.matrix[,colnames(con.matrix)[ii]]); > show(summary(de <- decideTestsDGE(currLrt))); > > limma-voom: > >design <- model.matrix(~0+RasTum); > > con.matrix<-makeContrasts( > + RasP.Tumor_RasPNot.Tumor=RasP.Tumor-RasPNot.Tumor, > + RasOnly.Tumor_RasPNot.Tumor=RasOnly.Tumor-RasPNot.Tumor, > + RasOnly.Normal_RasPNot.Normal=RasOnly.Normal-RasPNot.Normal, > + RasP.Normal_RasPNot.Normal=RasP.Normal-RasPNot.Normal, > + levels=design) > > fit<- lmFit(v,design) > > fit<-contrasts.fit(fit, con.matrix) > > fit <- eBayes(fit) > > show(summary(de <- decideTests(fit))); > > > [[alternative HTML version deleted]] > > > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:15}}
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